Understanding your edge
Winslow
Junior Member
I read through a Mailbag on The Power Rank today and this part jumped out to me:
The main part in the Bob Stoll link was this:
Under what Stoll outlines, it looks like it'd take 5 points of difference between his line and the Vegas line to get up to even a 55% chance of covering the spread, which seems way different than what The Power Rank said (where a 2.5-point difference led to a 58.4% chance of covering the spread). Although we're talking about two different sports -- college football and NBA.
Can anyone here offer any insight on this? Does anyone have a better (more simple) explanation of how to determine your edge if you feel your line is better than the Vegas line?
When betting ATS, how do you calculate your advantage to a win probability? For example, if the Houston Rockets are +7.5 at the books but your numbers imply they are really only 5 point dogs, you would have a 2.5 point advantage. How would you turn that 2.5 points into the probability that Houston would then cover the spread?
Josh Burton, member.
To get a rough estimate, you could use the relationship between point spread and win probability.
For example, a NBA team favored by 2.5 points has a 58.4% win probability. If we make some blind assumptions about your Rockets game, then a 2.5 point edge from the model equates with a 58.4% chance to cover. However, this assumes your model is very good. Handicapper Bob Stoll discusses this issue in this article under Determining Value.
To get a better answer, you could develop a simulation of the game of basketball. This simulation would capture features like the rate at which teams take 3 pointers and foul at the end of games. Both of these factors affect the distribution of outcomes for the game.
A game simulation gives you a better estimate of the probability the team covers. However, this estimate is still at the mercy of the quality of the underlying model.
The main part in the Bob Stoll link was this:
I have 10 years using my current College Football math model and the equation to predict the chance that the home team covers the spread is .500 + 0.01 x LD, where LD is the line differential between my math model prediction and the line. So, for every point differential, I can add 1% to my chance of winning.
Under what Stoll outlines, it looks like it'd take 5 points of difference between his line and the Vegas line to get up to even a 55% chance of covering the spread, which seems way different than what The Power Rank said (where a 2.5-point difference led to a 58.4% chance of covering the spread). Although we're talking about two different sports -- college football and NBA.
Can anyone here offer any insight on this? Does anyone have a better (more simple) explanation of how to determine your edge if you feel your line is better than the Vegas line?
Comments
Very interesting 1st post. Did you do any back testing on your model for the 1 point difference?
Btw I very rarely bet professional sports as I usually just bet props and small markets but I bet for value the Houston money line last game.
Good luck to you.
Sorry, but are you saying that any good model would rarely be more than two points off the vegas number?
Jets,
I have to disagree with you here. We've seen it a couple of times over the last few seasons that a team's true strength can significantly differ from the vegas number. This won't happen continuously over the whole season, but it can happen in spurts. Coaching changes, player injuries or just teams jelling together can make for profitable NBA betting. I'll give you two examples:
- This year, the Atlanta Hawks started out at .500 in the month of November before jelling and they started beating teams by 15+ points night after night. Even in January when they were on a 23-4 ATS streak they were still being priced as underdogs in some road games. And they kept beating teams by 15+ points. It wasn't until the end of January when 'Vegas' had them priced according to their true strength.
- Last year, the Indiana Pacers started out on a tear, but they stumbled through the 2nd half. They were still priced like league leaders for February and March while they were performing like bottom feeders losing regularly by 10+ while they were favourites.
So no, in my opinion, if you'r model is 2 points off, it doesn't mean that you have to re-write the whole model. You might have a cash cow on your hands.
I don't disagree with you, it happens , I just pass on those games ,cash cow , am not sure about that , where do you draw the line ? though I was told by someone pretty sharp I should go ahead and bet those games blindly if there weren't any major injuries, of course I don't.
Maybe next year I look closer at those games.
Agree, with you
use a push chart like this http://www.sportsbookreview.com/betting-tools/half-point-calculator/
assuming a fair price of +5 -110, getting +7.5 should cost you about -172 which translates to about 63% win prob (again this is according to the sbr puch chart, which i dont necessarily agree with the implied push probabilities)
So lets say his model in the NBA has Houston +5 and the current market line is Houston +7.5 that would be a 2.5 point edge according to his model.
Now he can add 1% for each off point according to Dr.Bob / Stoll or whom ever wrote that article, which would be 2.5%
So if -110 is break even at 52.38% he would enter 54.88% in the below calc and either bet full kelly or half kelly.
http://www.albionresearch.com/kelly/default.php
PS: better make sure your lines are solid or full kelly will send you to battery park sweeping the streets in no time
LMAO. Btw, wasn't the guy Peter Lynch you mentioned the Fidelity Magellan guru that bought the market? Whatever happened to him and the fund?
Lets say you were really confident in your model and the push chart you use (like Sbr's). Could you do it this way...assume it says the fair price is -172, then you have a 62 cent edge getting -110. 162/262 = 61.8%. So enter that into Kelly. That recommends 20% which is nuts
It probably comes down to how much you trust your number. I haven't read how Bob came up with 1%. Its seem arbitrary to me but its probably superior to the method I suggested above.